text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is ... | 3,085 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
pooled_output = last_hidden_state[:, 0, :]
pooled_output = self.post_layernorm(pooled_output)
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPooling(
last_hidden_state=last_hidden_state,
pooler_o... | 3,085 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
class Blip2QFormerMultiHeadAttention(nn.Module):
def __init__(self, config, is_cross_attention=False):
super().__init__()
self.config = config
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
"The... | 3,086 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
self.query = nn.Linear(config.hidden_size, self.all_head_size)
if is_cross_attention:
self.key = nn.Linear(config.encoder_hidden_size, self.all_head_size)
self.value = nn.Linear(config.encoder_hidden_size, self.all_head_size)
else:
self.key = nn.Linear(config.hidden_s... | 3,086 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
def save_attn_gradients(self, attn_gradients):
self.attn_gradients = attn_gradients
def get_attn_gradients(self):
return self.attn_gradients
def save_attention_map(self, attention_map):
self.attention_map = attention_map
def get_attention_map(self):
return self.attention_m... | 3,086 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_value=None,
output_attentions=False,
):
# If this is instantiated as a cross-attention module, the keys
... | 3,086 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
if is_cross_attention:
key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
attention_mask = encoder_attention_mask
elif past_key_value is not None:
key_layer = self.trans... | 3,086 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
seq_length = hidden_states.s... | 3,086 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
if self.position_embedding_type == "relative_key":
relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
attention_scores = attention_scores + relative_position_scores
elif self.position_embedding_type == "relative_key_query":
... | 3,086 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
# Normalize the attention scores to probabilities.
attention_probs = nn.Softmax(dim=-1)(attention_scores)
if is_cross_attention and self.save_attention:
self.save_attention_map(attention_probs)
attention_probs.register_hook(self.save_attn_gradients)
# This is actually d... | 3,086 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
outputs = outputs + (past_key_value,)
return outputs | 3,086 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
class Blip2QFormerSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
... | 3,087 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
class Blip2QFormerAttention(nn.Module):
def __init__(self, config, is_cross_attention=False):
super().__init__()
self.attention = Blip2QFormerMultiHeadAttention(config, is_cross_attention)
self.output = Blip2QFormerSelfOutput(config)
self.pruned_heads = set()
def prune_heads(sel... | 3,088 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
# Update hyper params and store pruned heads
self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads)
self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads) | 3,088 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
... | 3,088 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
class Blip2QFormerIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
sel... | 3,089 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
class Blip2QFormerOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
... | 3,090 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
class Blip2QFormerLayer(nn.Module):
def __init__(self, config, layer_idx):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = Blip2QFormerAttention(config)
self.layer_idx = layer_idx
if layer_idx % ... | 3,091 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_value=None,
output_attentions=False,
query_length=0,
):
# decoder uni-directional self-attention ca... | 3,091 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
if self.has_cross_attention:
if encoder_hidden_states is None:
raise ValueError("encoder_hidden_states must be given for cross-attention layers")
cross_attention_outputs = self.crossattention(
query_attention_output,
attention_m... | 3,091 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
if attention_output.shape[1] > query_length:
layer_output_text = apply_chunking_to_forward(
self.feed_forward_chunk,
self.chunk_size_feed_forward,
self.seq_len_dim,
attention_output[:, query_length:, :],
)
... | 3,091 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
def feed_forward_chunk_query(self, attention_output):
intermediate_output = self.intermediate_query(attention_output)
layer_output = self.output_query(intermediate_output, attention_output)
return layer_output | 3,091 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
class Blip2QFormerEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList(
[Blip2QFormerLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
)
self.gradient_checkpointing = False
... | 3,092 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
for i in range(self.config.num_hidden_layers):
layer_module = self.layer[i]
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_head_mask = head_mask[i] if head_mask is not None else None
past_key_value = past_key_value... | 3,092 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
if getattr(self.config, "gradient_checkpointing", False) and self.training:
if use_cache:
logger.warning(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
... | 3,092 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
) | 3,092 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[-1],)
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
if layer_module.has_cross_attention:
all_cross_atten... | 3,092 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
if not return_dict:
return tuple(
v
for v in [
hidden_states,
next_decoder_cache,
all_hidden_states,
all_self_attentions,
all_cross_attentions,
]
... | 3,092 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
class Blip2TextEmbeddings(nn.Module):
"""Construct the embeddings from word and position embeddings."""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.position_embeddings = n... | 3,093 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
def forward(
self,
input_ids: Optional[torch.FloatTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
query_embeds: Optional[torch.FloatTensor] = None,
) -> torch.Tensor:
if input_ids is not None:
seq_length = input_ids.size()[1]
else:
... | 3,093 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
class Blip2QFormerModel(Blip2PreTrainedModel):
"""
Querying Transformer (Q-Former), used in BLIP-2.
"""
def __init__(self, config: Blip2QFormerConfig):
super().__init__(config)
self.config = config
self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
... | 3,094 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
def get_extended_attention_mask(
self,
attention_mask: torch.Tensor,
input_shape: Tuple[int],
device: torch.device,
has_query: bool = False,
) -> torch.Tensor:
"""
Makes broadcastable attention and causal masks so that future and masked tokens are ignored.
... | 3,094 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
Returns:
`torch.Tensor` The extended attention mask, with a the same dtype as `attention_mask.dtype`.
"""
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
... | 3,094 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and -10000.0 for masked positions.
# Since we are adding it to the raw scores before the softmax, this is
# eff... | 3,094 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
def forward(
self,
query_embeds: torch.FloatTensor,
query_length: Optional[int] = None,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_... | 3,094 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, `optional`):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in `[0, ... | 3,094 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape
`(batch_size, sequence_length)`.
use_cache (`bool`, `optional`):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
... | 3,094 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
# past_key_values_length
past_key_values_length = (
past_key_values[0][0].shape[2] - self.config.query_length if past_key_values is not None else 0
)
query_length = (
query_length if query_length is not None else query_embeds.shape[1] if query_embeds is not None else 0
... | 3,094 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape, device)
# If a 2D or 3D attenti... | 3,094 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
if isinstance(encoder_attention_mask, list):
encoder_extended_attention_mask = [self.invert_attention_mask(mask) for mask in encoder_attention_mask]
elif encoder_attention_mask is None:
encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
e... | 3,094 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_lengt... | 3,094 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
encoder_outputs = self.encoder(
embedding_output,
attention_mask=extended_attention_mask,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_extended_attention_mask,
past_key_values=past_key_values,
... | 3,094 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
return BaseModelOutputWithPoolingAndCrossAttentions(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
past_key_values=encoder_outputs.past_key_values,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
... | 3,094 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
class Blip2Model(Blip2PreTrainedModel):
config_class = Blip2Config
main_input_name = "pixel_values"
def __init__(self, config: Blip2Config):
super().__init__(config)
self.vision_model = Blip2VisionModel(config.vision_config)
self.query_tokens = nn.Parameter(torch.zeros(1, config.n... | 3,095 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
self.language_model = language_model
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.language_model.get_input_embeddings()
def set_input_embeddings(self, value):
self.language_model.set_input_embeddings(value)
d... | 3,095 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
@add_start_docstrings_to_model_forward(BLIP_2_TEXT_INPUTS_DOCSTRING)
def get_text_features(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids: Optional[torch.Tensor] = None,
decoder_attention_mask: Optional[torch.... | 3,095 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
>>> from transformers import AutoTokenizer, Blip2Model | 3,095 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
>>> model = Blip2Model.from_pretrained("Salesforce/blip2-opt-2.7b")
>>> tokenizer = AutoTokenizer.from_pretrained("Salesforce/blip2-opt-2.7b")
>>> inputs = tokenizer(["a photo of a cat"], padding=True, return_tensors="pt")
>>> text_features = model.get_text_features(**inputs)
```"""
... | 3,095 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
if self.config.use_decoder_only_language_model:
text_outputs = self.language_model(
input_ids=input_ids,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=... | 3,095 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
@add_start_docstrings_to_model_forward(BLIP_2_VISION_INPUTS_DOCSTRING)
def get_image_features(
self,
pixel_values: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
... | 3,095 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
>>> processor = AutoProcessor.from_pretrained("Salesforce/blip2-opt-2.7b")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = processor(images=image, return_tensors="pt")
>>> image_outputs = model.get... | 3,095 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
vision_outputs = self.vision_model(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
interpolate_pos_encoding=interpolate_pos_encoding,
)
return vision_outputs | 3,095 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
@add_start_docstrings_to_model_forward(BLIP_2_INPUTS_DOCSTRING)
def get_qformer_features(
self,
pixel_values: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
... | 3,095 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
>>> processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
>>> model = Blip2Model.from_pretrained("Salesforce/blip2-opt-2.7b")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = proc... | 3,095 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
vision_outputs = self.vision_model(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
interpolate_pos_encoding=interpolate_pos_encoding,
)
image_embeds = vision_... | 3,095 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
@add_start_docstrings_to_model_forward(BLIP_2_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Blip2ForConditionalGenerationModelOutput, config_class=Blip2VisionConfig)
def forward(
self,
pixel_values: torch.FloatTensor,
input_ids: torch.FloatTensor,
attention_mask: Optio... | 3,095 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
>>> device = "cuda" if torch.cuda.is_available() else "cpu"
>>> processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
>>> model = Blip2Model.from_pretrained("Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16)
>>> model.to(device) # doctest: +IGNORE_RESULT
>>> url... | 3,095 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
# step 1: forward the images through the vision encoder,
# to get image embeddings of shape (batch_size, seq_len, hidden_size)
vision_outputs = self.vision_model(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states... | 3,095 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1)
query_outputs = self.qformer(
query_embeds=query_tokens,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_attention_mask,
output_attentions=output_attentions,
output... | 3,095 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
if attention_mask is None:
attention_mask = torch.ones_like(input_ids)
expected_device = language_model_attention_mask.device
attention_mask = torch.cat([language_model_attention_mask, attention_mask.to(expected_device)], dim=1) | 3,095 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
if self.config.use_decoder_only_language_model:
outputs = self.language_model(
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_di... | 3,095 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
loss = loss_fct(shift_logits.view(-1, self.config.text_config.vocab_size), shift_labels.view(-1))
else:
outputs = self.language_model(
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
deco... | 3,095 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
return Blip2ForConditionalGenerationModelOutput(
loss=loss,
logits=logits,
vision_outputs=vision_outputs,
qformer_outputs=query_outputs,
language_model_outputs=outputs,
) | 3,095 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
class Blip2TextModelWithProjection(Blip2PreTrainedModel):
supports_gradient_checkpointing = False
_keep_in_fp32_modules = []
def __init__(self, config: Blip2Config):
super().__init__(config)
self.query_tokens = nn.Parameter(torch.zeros(1, config.num_query_tokens, config.qformer_config.hidd... | 3,096 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
@add_start_docstrings_to_model_forward(BLIP_2_TEXT_WITH_PROJECTION_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Blip2TextModelOutput, config_class=Blip2Config)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
... | 3,096 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
>>> processor = AutoProcessor.from_pretrained("Salesforce/blip2-itm-vit-g")
>>> inputs = processor(text=["a photo of a cat", "a photo of a dog"], return_tensors="pt").to(device)
>>> outputs = model(**inputs)
>>> text_embeds = outputs.text_embeds
>>> print(text_embeds.shape)
tor... | 3,096 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
text_embeds = self.text_projection(pooled_output)
text_embeds = nn.functional.normalize(text_embeds, dim=-1)
if not return_dict:
outputs = (text_embeds, text_outputs[0]) + text_outputs[2:]
return tuple(output for output in outputs if output is not None)
return Blip2Text... | 3,096 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
class Blip2VisionModelWithProjection(Blip2PreTrainedModel):
main_input_name = "pixel_values"
_keep_in_fp32_modules = []
def __init__(self, config: Blip2Config):
super().__init__(config)
self.vision_model = Blip2VisionModel(config.vision_config)
self.query_tokens = nn.Parameter(tor... | 3,097 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
@add_start_docstrings_to_model_forward(BLIP_2_VISION_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Blip2VisionModelOutput, config_class=Blip2Config)
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hi... | 3,097 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
>>> processor = AutoProcessor.from_pretrained("Salesforce/blip2-itm-vit-g")
>>> model = Blip2VisionModelWithProjection.from_pretrained(
... "Salesforce/blip2-itm-vit-g", torch_dtype=torch.float16
... )
>>> model.to(device) # doctest: +IGNORE_RESULT
>>> url = "http://images.... | 3,097 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
>>> outputs = model(**inputs)
>>> image_embeds = outputs.image_embeds
>>> print(image_embeds.shape)
torch.Size([1, 32, 256])
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
... | 3,097 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
query_tokens = self.query_tokens.expand(pooled_output.shape[0], -1, -1)
query_outputs = self.qformer(
query_embeds=query_tokens,
encoder_hidden_states=pooled_output,
encoder_attention_mask=image_attention_mask,
return_dict=return_dict,
)
embeds =... | 3,097 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
class Blip2ForConditionalGeneration(Blip2PreTrainedModel, GenerationMixin):
config_class = Blip2Config
main_input_name = "pixel_values"
def __init__(self, config: Blip2Config):
super().__init__(config)
self.vision_model = Blip2VisionModel(config.vision_config)
self.query_tokens = ... | 3,098 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
# Update _tied_weights_keys using the base model used.
if language_model._tied_weights_keys is not None:
self._tied_weights_keys = [f"language_model.{k}" for k in language_model._tied_weights_keys]
self.language_model = language_model
# Initialize weights and apply final processing... | 3,098 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
def _tie_weights(self):
if not self.config.use_decoder_only_language_model:
self.language_model.encoder.embed_tokens = self.language_model.shared
self.language_model.decoder.embed_tokens = self.language_model.shared
def _preprocess_accelerate(self):
r"""
Some pre-pro... | 3,098 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
if len(hf_device_map) > 1 and "language_model" not in hf_device_map and torch.cuda.device_count() > 1:
# warn users about unexpected behavior when using multi-GPU + BLIP-2 + `accelerate`.
logger.warning(
"The `language_model` is not in the `hf_device_map` dictionary and you are r... | 3,098 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
@add_start_docstrings_to_model_forward(BLIP_2_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Blip2ForConditionalGenerationModelOutput, config_class=Blip2VisionConfig)
def forward(
self,
pixel_values: torch.FloatTensor,
input_ids: torch.FloatTensor,
attention_mask: Optio... | 3,098 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import Blip2Processor, Blip2ForConditionalGeneration
>>> import torch
>>> device = "cuda" if torch.cuda.is_available() else "cpu"
>>> processor = Blip2Processor.from_pretrained("Salesforce/bli... | 3,098 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
>>> generated_ids = model.generate(**inputs)
>>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
>>> print(generated_text)
two cats laying on a couch
```
Visual question answering (prompt = question):
```python
>>> pro... | 3,098 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
```python
>>> model = Blip2ForConditionalGeneration.from_pretrained(
... "Salesforce/blip2-opt-2.7b", load_in_8bit=True, device_map={"": 0}, torch_dtype=torch.bfloat16
... ) # doctest: +IGNORE_RESULT
>>> inputs = processor(images=image, text=prompt, return_tensors="pt").to(device="... | 3,098 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
# step 1: forward the images through the vision encoder,
# to get image embeddings of shape (batch_size, seq_len, hidden_size)
vision_outputs = self.vision_model(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states... | 3,098 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1)
query_outputs = self.qformer(
query_embeds=query_tokens,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_attention_mask,
output_attentions=output_attentions,
output... | 3,098 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
# if the model already has "image_token_index" then the input is expanded to account for image embeds
# otherwise we expand manually by concating
if getattr(self.config, "image_token_index", None) is not None:
special_image_mask = (input_ids == self.config.image_token_index).unsqueeze(-1).ex... | 3,098 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
inputs_embeds = torch.cat([language_model_inputs, inputs_embeds.to(language_model_inputs.device)], dim=1)
attention_mask = torch.cat(
[language_model_attention_mask, attention_mask.to(language_model_attention_mask.device)], dim=1
) | 3,098 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
if self.config.use_decoder_only_language_model:
outputs = self.language_model(
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_di... | 3,098 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
loss = loss_fct(shift_logits.view(-1, self.config.text_config.vocab_size), shift_labels.view(-1))
else:
outputs = self.language_model(
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
deco... | 3,098 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
return Blip2ForConditionalGenerationModelOutput(
loss=loss,
logits=logits,
vision_outputs=vision_outputs,
qformer_outputs=query_outputs,
language_model_outputs=outputs,
)
@torch.no_grad()
def generate(
self,
pixel_values: torch... | 3,098 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
Args:
pixel_values (`torch.FloatTensor` of shape (batch_size, num_channels, height, width)):
Input images to be processed.
input_ids (`torch.LongTensor` of shape (batch_size, sequence_length), *optional*):
The sequence used as a prompt for the generation.
... | 3,098 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
batch_size = pixel_values.shape[0]
image_embeds = self.vision_model(
pixel_values,
return_dict=True,
interpolate_pos_encoding=interpolate_pos_encoding,
).last_hidden_state
image_attention_mask = torch.ones(image_embeds.size()[:-1], dtype=torch.long, device=ima... | 3,098 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
if input_ids is None:
start_tokens = [self.config.text_config.bos_token_id]
if getattr(self.config, "image_token_index", None) is not None:
start_tokens = [self.config.image_token_index] * self.config.num_query_tokens + start_tokens
input_ids = torch.tensor([start_tok... | 3,098 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
# if the model already has "image_token_index" then the input is expanded to account for image embeds
# otherwise we expand manually by concatenating
if getattr(self.config, "image_token_index", None) is not None:
special_image_mask = (input_ids == self.config.image_token_index).unsqueeze(-1... | 3,098 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
[language_attention_mask, attention_mask.to(language_attention_mask.device)], dim=1
) | 3,098 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
# add image_embeds length to max_length, so that the final max_length in counted only on token embeds
# -1 is to account for the prepended BOS after `generate.`
# TODO (joao, raushan): refactor `generate` to avoid these operations with VLMs
if not self.language_model.config.is_encode... | 3,098 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
class Blip2ForImageTextRetrieval(Blip2PreTrainedModel):
main_input_name = "pixel_values"
_keep_in_fp32_modules = []
def __init__(self, config: Blip2Config):
super().__init__(config)
self.vision_model = Blip2VisionModel(config.vision_config)
self.query_tokens = nn.Parameter(torch.z... | 3,099 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
def get_input_embeddings(self):
return self.embeddings.word_embeddings
def set_input_embeddings(self, value):
self.embeddings.word_embeddings = value
@add_start_docstrings_to_model_forward(BLIP2_IMAGE_TEXT_RETRIEVAL_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Blip2ImageTextMat... | 3,099 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
```python
>>> import torch
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, Blip2ForImageTextRetrieval
>>> device = "cuda" if torch.cuda.is_available() else "cpu"
>>> model = Blip2ForImageTextRetrieval.from_pretrained("Salesforce... | 3,099 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
>>> inputs = processor(images=image, text=text, return_tensors="pt").to(device, torch.float16)
>>> itm_out = model(**inputs, use_image_text_matching_head=True)
>>> logits_per_image = torch.nn.functional.softmax(itm_out.logits_per_image, dim=1)
>>> probs = logits_per_image.softmax(dim=1) # we ca... | 3,099 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
>>> inputs = processor(images=image, text=texts, return_tensors="pt").to(device, torch.float16)
>>> itc_out = model(**inputs, use_image_text_matching_head=False)
>>> logits_per_image = itc_out.logits_per_image # this is the image-text similarity score
>>> probs = logits_per_image.softmax(dim=1)... | 3,099 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
vision_outputs = self.vision_model(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
image_embeds = vision_outputs[0]
image_attention_mask = torch.ones(image_embe... | 3,099 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
text_outputs = self.qformer(
query_embeds=query_embeds,
query_length=query_tokens.shape[1],
attention_mask=attention_mask,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_attention_mask,
return_dict=return_d... | 3,099 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.